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Physics descriptors enhanced Bayesian learning method for permeability of random media under sparse data

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 143卷

作者： Qi, Hang Guan, Xiaofei Chen, Qing Jiang, Zhengwu Liu, Fang Zhang, Jieqiong Zhu, Hehua Tongji Univ Sch Math Sci Minist Educ Shanghai 200092 Peoples R China Tongji Univ Key Lab Intelligent Comp & Applicat Minist Educ Shanghai 200092 Peoples R China Henan Acad Sci Inst Math Zhengzhou 450046 Peoples R China Tongji Univ Key Lab Adv Civil Engn Mat Minist Educ Shanghai 200092 Peoples R China Tongji Univ Sch Mat Sci & Engn Shanghai 200092 Peoples R China Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China Northwest Univ Sch Math Xian 710127 Peoples R China Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai 200092 Peoples R China

Permeability is a significant property in microstructure-based material design. Currently, the main research gap in such material design can be divided into three aspects: Firstly, experimental methods face stringent conditions and poor reproducibility in accurately describing internal media structures. Secondly, machine learning methods require impractically large datasets to learn the strong nonlinear relationship between permeability and microstructure. Thirdly, there is a challenge in quantifying the inherent uncertainty of randomly distributed phases and the uncertainty propagation in the model. In this work, we propose a novel physics descriptors enhanced Bayesian learning method, aiming to predict the permeability of random media under sparse data. The key aspects of the method are the physics descriptors with clear physical indications, which characterize porous microstructure from multiple perspectives, and an encoder-decoder strategy that reduces microstructural complexity while utilizing these physics descriptors as basis functions to reconstruct permeability. The numerical experiments indicate that porosity, chord length density, and lineal-path dominate in determining permeability. Moreover, predictions with a 5%-11% error can be achieved under 32 training samples. Finally, the method provides a complete posterior distribution, where the posterior variances quantify uncertainty and benefit robust decisions in simulation-assisted materials design.

关键词： Permeability Physics descriptors Bayesian learning encoder-decoder Structure-property links Simulation-assisted materials design sparse data Uncertainty quantification

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Investigation of data-driven model predictive control for liquid nitrogen cooling on helium refrigerator

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FUSION ENGINEERING AND DESIGN 2025年 211卷

作者： Yu, Qiang Zhou, Zhiwei Yuan, Kai Li, Shanshan Zhu, Zhigang Zhuang, Ming Chinese Acad Sci Inst Plasma Phys Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

The helium refrigerator, which is a critical infrastructure of the fusion device, should be controlled well and maintain stability. During the operation of one refrigerator in the Comprehensive Research Facility for Fusion Technology, a continuous oscillation behavior was observed in the liquid nitrogen (LN2) cooling system. This paper explores a data-driven Model Predictive Control (MPC) scheme for the LN2 cooling control. Modeling the complex system dynamics under the oscillation disturbance is achieved by the encoder-decoder recurrent neural network, which provides an end-to-end implementation for multistep prediction. The data-driven MPC applies the particle swarm optimization algorithm to find the optimal control actions, in which a novelty particle initialization method is adopted to improve the search efficiency. The performance of the data-driven MPC is evaluated by closed-loop simulation, and the simulation results indicate that the disturbance can be effectively restrained. The proposed scheme shows a promising extension prospect, such as smoothing the pulse heat load disturbance in the fusion cryogenic system.

关键词： Model predictive control encoder-decoder Recurrent neural network Liquid nitrogen cooling Helium refrigerator

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ERU-Net: A novel effective 2D residual neural network for brain tumors semantic segmentation from multimodal MRI

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 101卷

作者： Mohammed, Yahya M. A. Jellouli, Ismail El Garouani, Said Abdelmalek Essaadi Univ Fac Sci Dept Comp Sci Emerging Comp Technol ECT Tetouan Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci Fes Morocco

Brain tumor segmentation technology is crucial in diagnosing and treating MRI brain tumors. It aids doctors in locating and measuring tumors. It also plays a vital role in the planning and development of treatment and rehabilitation strategies. Recently, challenges have persisted despite the success achieved by deep learning- based methods in improving the segmentation accuracy of MRI brain tumor regions. These challenges are particularly prominent in small-scale tumor regions, where issues arise due to their diminutive size and the substantial variation between regions occupied by different tumor classes. Additionally, limitations in parameters and computational complexity contribute to these challenges. Thus, there remains considerable potential for enhancing these methods and increasing their efficacy. This paper presents an effective 2D residual neural network (2D ERU-Net) for segmenting MRI brain tumors. The proposed architecture consists of residual units in the encoder path to speedup training and convergence, and a deep supervision module (DSM) in the decoder path to address gradient-related issues and obtain high-resolution feature maps. Furthermore, a fusion weighted loss function, incorporating both weighted dice loss and weighted cross-entropy loss, is developed to solve the issues related to network convergence and data imbalance. The results of experiments on the reliable MRI brain tumor BraTS2019 dataset showed that the ERU-Net achieved a mean dice coefficient of 0.881, 0.882, and 0.891 and Hausdorff distances of 20.86 mm, 17.83 mm, and 13.35 mm for the tumor core, enhancing tumor, and whole tumor, respectively. Furthermore, ERU-Net demonstrates exceptional efficiency when compared to state-of-the-art methods.

关键词： 2D convolutional neural networks Automatic segmentation Brain tumor segmentation encoder-decoder Residual block Semantic segmentation Brain MRI

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MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for segmentation of polyps in colonoscopy

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 102卷

作者： Raghaw, Chandravardhan Singh Yadav, Aryan Sanjotra, Jasmer Singh Dangi, Shalini Kumar, Nagendra Indian Inst Technol IIT Indore Dept Comp Sci & Engn Indore 453552 India Indian Inst Technol IIT Indore Dept Elect Engn Indore 453552 India

Automated polyp segmentation in colonoscopy is crucial for early diagnosis and treatment planning of colorectal cancer. Early detection of polyps significantly improves survival rates. However, traditional methods rely on manual verification by endoscopic physicians, increasing the risk of missed polyps due to their variability. To address this, we propose a novel Multiscale Network with Spatial-enhanced Attention (MNetSAt) for automated polyp segmentation. MNet-SAt preserves edge information to improve polyp boundary detection and efficiently utilizes multi-scale features across spatial and channel dimensions, focusing on key regions. Additionally, a spatially enhanced attention mechanism captures spatial relationships and global context, further improving segmentation accuracy. We comprehensively evaluate the efficacy of MNet-SAt on the Kvasir-SEG and CVC-ClinicDB datasets. Qualitative and quantitative experiments demonstrate the superiority of MNet-SAt over ten state-of-the-art existing baselines and its stronger generalization performance. On Kvasir-SEG, it achieves an outstanding performance of 96.61% DSC, 96.92% IoU, 97.36% Precision, 97.83% Accuracy, and 97.12% Recall. On CVC-ClinicDB, it achieves 98.60% DSC, 95.89% IoU, 96.89% Precision, 97.32% Accuracy, and 96.13% Recall. In summary, MNet-SAt offers remarkable performance in automating polyp segmentation. The superior performance of MNet-SAt shows great potential for improving the diagnostic process in early polyp identification and more effective treatment planning, thereby contributing to lowering colorectal cancer mortality rates.

关键词： Polyp segmentation Multi-scale features Spatial attention encoder-decoder Boundary preservation

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MF-EDNet: predicting stock market sector indices based on multi-feature fusion under emergency events

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2025年 1-22页

作者： Han, Tianjiao Yuan, Chenxun Wang, Pengcheng Hao, Xingwei Guo, Fenghua UCL Math Dept Gower St London WC1E 6AE England Shandong Univ Sch Software Jinan 250101 Peoples R China

The impact of emergency events on the stock market cannot be underestimated, as their unpredictability poses significant challenges to investors' stock operations. This calls for researchers and investors to seek more effective features and reasonable methods to mitigate risks. In the context of multi-feature prediction methods, analyzing the correlation between multi-dimensional features or data has always been a challenging issue. This paper proposes a stock market index prediction framework based on an encoder-decoder architecture (MF-EDNet). The framework leverages the dynamic correlation between stock data and futures data as prior knowledge, integrating features of both internal sequences (industry indices) and external sequences (futures data) to capture the impact of emergency events on the stock market. The newly proposed Multi-Dimensional Convolutional Attention Module (MCAM) further enhances the feature extraction and attention capabilities of the attention mechanism. Experiments on multiple industry indices in the Chinese stock market demonstrate that MF-EDNet can effectively extract important features from stock and futures data, exhibiting good predictive performance under emergency events. The proposed MF-EDNet model achieved improvements of 35.8% and 22.9% in the Matthews correlation coefficient (MCC), a 3.3% increase in accuracy (ACC) and a 7.86% enhancement in profit compared to previous state-of-the-art methods.

关键词： Emergency events Stock trend prediction Feature fusion MCAM encoder-decoder

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R-UAV-Net: Enhanced YOLOv4 With Graph-Semantic Compression for Transformative UAV Sensing in Paddy Agronomy

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 1197-1209页

作者： Sangaiah, Arun Kumar Anandakrishnan, Jayakrishnan Devarapelly, Aniruth Reddy Bin Mohamad, Muhammad Luqman Arif Bian, Gui-Bin Alenazi, Mohammed J. F. AlQahtani, Salman A. Natl Yunlin Univ Sci & Technol Int Grad Sch AI Touliu 64002 Taiwan Sunway Univ Sch Engn & Technol Subang Jaya 47500 Malaysia Univ Ctr Res & Dev Chandigarh Univ Mohali 140413 India Natl Inst Technol Puducherry Dept Comp Sci & Engn Karaikal 609605 India Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Srivilliputhur 626190 India UPM Dept Math Fac Sci Seri Kembangan 43400 Malaysia Chinese Acad Sci Inst Automation State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China King Saud Univ Coll Comp & Informat Sci Comp Engn Dept Riyadh 11543 Saudi Arabia

Common leaf diseases pose severe problems to the agricultural industry, particularly for paddy rice, a staple crop consumed worldwide, making early detection and rapid prevention crucial for maintaining both quality and yield. This research dwells on the object detection farmwork for identifying and localising paddy leaf diseases. Future-tech Unmanned Aerial Vehicles (UAVs) offer benefits such as reduced deployment costs, increased availability, enhanced operability, and improved geographical and temporal resolution. You Only Look Once (YOLO) models excel in disease part detection but require excessive computing. A severe challenge of UAV sensing is the resource-efficient collection, transmission and disease detection from this high-resolution ground data. This research addresses these issues by introducing a Graph-inspired encoder-decoder Semantic Compression (G-SC) coupled with enhanced YOLOv4 architecture for disease detection in paddy agronomy. The proposed R-UAV-Net is an improved YOLOv4 architecture incorporating various spatial and channel feature extraction blocks with attention mechanisms for revolutionizing precision farming. R-UAV-Net outperformed state-of-the-art (SOTA) techniques, showing a 0.69% improvement in mean average precision (mAP) and a 0.12 increase in F1 score over the best-performing leaf detection model.

关键词： Diseases YOLO Feature extraction Autonomous aerial vehicles Accuracy Real-time systems Farming UAV remote sensing YOLOv4 semantic compression (G-SC) encoder-decoder precision farming leaf disease detection

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A fast solution method for the Dynamic Flexible Pickup and Delivery Problem with task allocation fairness for multiple vehicles

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NEUROCOMPUTING 2025年 639卷

作者： Sun, Zhihui Tian, Ran Wu, Jiarui Lu, Xin Co, Jinshi Wang Northwest Normal Univ Dept Comp Sci & Engn Lanzhou 730070 Peoples R China

The Dynamic Flexible Pickup and Delivery Problem (DFPDP) originates from the actual needs of multi warehouse management strategies and is one of the important challenges currently facing the field of logistics and distribution. In DFPDP, it is necessary to address dynamic order fluctuations, quickly plan heterogeneous fleet routes, ensure fairness in task allocation, and minimize total travel time under time window constraints. However, there is currently little research on this issue, and traditional heuristic algorithms make it difficult to quickly find a solution to this problem. First, we propose a Multimodal Constraint Dynamic Scheduling Mechanism (MCDSM) to select a vehicle with the lowest current time consumption to make task allocation between vehicles as fair as possible. Second, we propose a Parallel encoder-Serial decoder model integrating Variable-length Sequences (PESDVS), in which the variable-length sequences designed can effectively handle the generation of dynamic orders and the changes in the number of pickup and delivery locations, while the trained model can adapt itself to different order scenarios. In addition, the model improves the quality order decisions through a parallel encoder and serial decoder structure to minimize the total traveling time of the fleet. Extensive experimental results demonstrate that our method has excellent performance and good generalization ability under different order sizes. At the same time, compared with heuristic algorithms, our method can quickly find a feasible solution to the problem and the task allocation between vehicles is relatively fair.

关键词： Pickup and delivery problem Heterogeneous fleets Deep reinforcement learning encoder-decoder

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Patch Attention U-Net for knee cartilage segmentation in magnetic resonance images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 106卷

作者： Wang, Xiang Shi, Cao Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266000 Shandong Peoples R China

Knee cartilage segmentation in magnetic resonance images is a challenging task with significant clinical implications for the diagnosis and treatment of osteoarthritis. Recent advances in deep convolutional neural networks (CNNs) have shown promise in improving the accuracy of knee segmentation. However, CNNs often struggle with small samples with irregular shapes, which may result in the omission of important features such as cartilage. In this study, we propose a novel network for knee cartilage segmentation that incorporates Patch Attention (PA) block to improve the ability of network to detect small objects and a Feature Aggregation block to fuse the features from same level of the encoder and previous layer of the decoder. The PA block includes Patch-based Channel-wise Attention block and Patch-based Patch-wise Attention block, which capture intrachannel and intra-patch relationships, respectively. Our method is evaluated on publicly available datasets the 2010 Grand Challenge Knee Image Segmentation (SKI-10) dataset and Osteoarthritis Initiative (OAI) dataset, and the results demonstrate that our approach achieves impressive performance in knee cartilage segmentation.

关键词： Deep learning encoder-decoder Hybrid loss Knee cartilage segmentation Patch Attention

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Two-Stage Residual Projection Network for Image Bit-Depth Enhancement

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IEEE ACCESS 2025年 13卷 31215-31227页

作者： Liu, Zepeng Wang, Yizong Sang, Yayuan Liu, Chao Tian, Jiya Xinjiang Inst Technol Sch Informat Engn Aksu 843100 Peoples R China

Low bit-depth (LBD) images produce stubborn false contour artifacts and make detailed information disappear, making bit-depth enhancement (BDE) a challenging task. Considering the mixture of structural distortions and real edges in LBD images, multi-scale features are crucial for the BDE tasks. However, existing CNN-based methods suffer from structural bottlenecks, which make it difficult to capture sufficient LBD features in a single-stage network. To overcome this issue, this paper proposes a two-stage residual projection network (TRPN) to explore the multi-scale features of BDE. An encoder-decoder structure based on alternating up and down sampling is proposed to learn wide context information in stage 1. In stage 2, a residual projection module based on dense connection is proposed to preserve the detailed texture as much as possible and avoid over-smoothing in non-flat regions, which is caused by alternating up and down sampling. To efficiently utilize multi-scale features, we introduce a supervised attention module that improves network ability by dynamically adjusting the attention weights within the model. Finally, extensive experiments demonstrate that our method achieves outstanding performance improvements both quantitatively and qualitatively, which illustrates its effectiveness.

关键词： Feature extraction Convolution Image color analysis Image reconstruction Distortion Deconvolution Training Signal processing algorithms Colored noise Visualization Bit-depth enhancement multi-scale features encoder-decoder alternating up and down sampling residual projection

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Tissue segmentation for traumatic brain injury based on multimodal MRI image fusion-semantic segmentation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 99卷

作者： Xu, Yao Chen, Zhongmin Wang, Xiaohui Jiang, Shanghai Wang, Fuping Lu, Hong Chongqing Univ Technol Sch Pharm & Bioengn Chongqing 400054 Peoples R China Chongqing Univ Technol Peoples Hosp Chongqing 7 Cent Hosp Dept Med Imaging Chongqing 400054 Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Chongqing Univ Technol Chongqing Key Lab Opt Fiber Sensor & Photoelect De Chongqing 400051 Peoples R China

Accurate segmentation of traumatic brain injury (TBI) has great significance for physicians to diagnose and assess a patient's condition. The utilization of multimodal information plays a critical role in TBI segmentation. However, most of the existing methods mainly focus on direct extraction and selection of deep semantic features, whereas in this paper, we use image fusion as an auxiliary task for feature learning based on multimodal feature extraction to achieve more sufficient fusion of multimodal features. Therefore, we design a multimodal image fusion-semantic segmentation based framework. The proposed approach mainly consists of a semantic encoder module, a semantic segmentation module and an image fusion module. The semantic encoder compresses the input image into a smaller feature space to extract semantic features. The semantic segmentation module utilizes both the detailed information extracted by the encoder and the semantic information of high-level features extracted from the semantic segmentation module to generate the segmentation results. The image fusion module fuses semantic feature information from different modalities as an auxiliary task to semantic segmentation. Furthermore, to enhance the model's performance even further, an uncertainty-based approach is employed, which dynamically adjusts the loss weights for the image fusion task and the semantic segmentation task during the model training process. The proposed method is evaluated on a private dataset, and compared with other widely recognized methods. It demonstrates outstanding performance in both Dice score and Recall metrics.

关键词： Traumatic brain injury segmentation Image fusion Multimodal MRI encoder-decoder Deep learning

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